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On the role of data...
On the role of data anonymization in machine learning privacy
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- Senavirathne, Navoda (författare)
- Högskolan i Skövde,Institutionen för informationsteknologi,Forskningsmiljön Informationsteknologi,Skövde Artificial Intelligence Lab (SAIL),School of Informatics, University of Skövde, Sweden
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- Torra, Vicenç (författare)
- Umeå universitet,Institutionen för datavetenskap,Department of Computer Science, University of Umeå, Sweden
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(creator_code:org_t)
- IEEE, 2020
- 2020
- Engelska.
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Ingår i: Proceedings - 2020 IEEE 19th International Conference on Trust, Security and Privacy in Computing and Communications, TrustCom 2020. - : IEEE. - 9780738143804 - 9780738143811 ; , s. 664-675
- Relaterad länk:
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https://urn.kb.se/re...
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https://doi.org/10.1...
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Abstract
Ämnesord
Stäng
- Data anonymization irrecoverably transforms the raw data into a protected version by eliminating direct identifiers and removing sufficient details from indirect identifiers in order to minimize the risk of re-identification when there is a requirement for data publishing. Nevertheless, data protection laws (i.e., GDPR) do not consider anonymized data as personal data thus allowing them to be freely used, analysed, shared and monetized without a compliance risk. Motivated by the above advantages, it is plausible that the data controllers anonymize the data before releasing them for any data analysis tasks such as machine learning (ML); which is applied in a wide variety of domains where personal data are used. Moreover, in recent research, it has shown that ML models are vulnerable to privacy attacks as they retain sensitive information from the training data. Taking all of these facts into consideration, in this work we explore the interplay between data anonymization and ML with the ultimate aim of clarifying whether data anonymization is sufficient to achieve privacy for ML under different adversarial scenarios. We also discuss the challenges and opportunities of integrating these two domains. As per our findings, it is conspicuous that in order to substantially minimize the privacy risks in ML, existing data anonymization techniques have to be applied with high privacy levels that cause a deterioration in model utility.
Ämnesord
- NATURVETENSKAP -- Data- och informationsvetenskap -- Datavetenskap (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Computer Sciences (hsv//eng)
- TEKNIK OCH TEKNOLOGIER -- Elektroteknik och elektronik -- Datorsystem (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Electrical Engineering, Electronic Engineering, Information Engineering -- Computer Systems (hsv//eng)
Nyckelord
- Data anonymization
- Data privacy
- Privacy preserving machine learning
- Deterioration
- Machine learning
- Data controllers
- Data protection laws
- Data publishing
- Privacy Attacks
- Re identifications
- Recent researches
- Sensitive informations
- Privacy by design
- Skövde Artificial Intelligence Lab (SAIL)
- Skövde Artificial Intelligence Lab (SAIL)
Publikations- och innehållstyp
- ref (ämneskategori)
- kon (ämneskategori)
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